System for storage, querying, and analysis of time series data

ABSTRACT

A system for storing time series data includes an ingester that prepares metadata indices associated with blocks of incoming time series data and stores the blocks of data in a time series database and the indices in a separate index database. The time series database distributes storage of the data blocks among multiple data nodes. A query layer receives queries and uses the index database to determine which data blocks are needed to process the query, and then requests only those data blocks from the time series database. Processing of the query is performed within the time series database only on those data nodes that contain relevant data, and partial results are passed to an output layer for formation into a final query result.

TECHNICAL FIELD

The systems and techniques described include embodiments that relate totechniques and systems for storing, and retrieving time series data onlarge data systems. They also include embodiments that related toanalysis and processing of such data.

BACKGROUND DISCUSSION

As the ability to connect devices together via electronic networks hasincreased, it has become more desirable to collect, transmit and storeoperational data for various systems so that such data can be used foranalysis and optimization. For instance, industrial equipment oftencontains a variety of sensors that can monitor multiple parametersrelated to the operation and performance of the equipment many times persecond. Such sensors rapidly produce a large volume of time serieshistorical data points associated with the equipment.

Although such data can be very useful for determining how to better usethe equipment, the volume of data produced often requires storage thatis not located on, or even near, the equipment itself. When data frommultiple related sources is stored (for instance, data related to eachwindmill in a wind farm), the volume of data produced over time growseven more quickly.

Although raw storage of such data merely requires a fast enoughconnection to a large enough storage array, raw storage of such timeseries data does not provide a structure that is amenable to easyretrieval of specific data. Furthermore, when the entire body of dataneeds to be searched in order to find data meeting specific criteria,brute force search methods will be too slow and resource intensive toprovide effective results.

Therefore, in order to allow for effective storage and retrieval of suchtime series data, it may be desirable to provide system that allows forrapid storage as well as efficient search and retrieval of specifieddata from such systems. It may also be desirable to enable effectiveanalytic capabilities of such large bodies of time series data stored insuch systems.

BRIEF DESCRIPTION

In one aspect of an embodiment of a system in accordance with thedescription herein, a system for time series data storage, querying andanalysis is presented. The system includes a data generator, aningester, an index database, a time series database, a query layer, anevaluator, and an output handler. The data generator produces timestamped data associated with the behavior of a plurality of assets. Theingester receives the time stamped data from the data generator, andprocesses the received time stamped data to create a data block and anindex associated with the received time stamped data. An index databasestores the index generated by the ingester, and a time series databasestores the data blocks generated by the ingester across a plurality ofcomputing devices.

In another aspect of an embodiment of a system in accordance with thedescription, a query layer is provided that receives a query specifyingcriteria that define a set of data to be retrieved from the system andan analysis to be performed on that data. The query layer requests fromthe index database indices identifying the data blocks needed toevaluate the query. The query layer then prepares a sub-query that willproduce appropriate data matching the criteria when executed against thedata in the data blocks. The sub-query includes the criteria and alogical operation to be performed on the data matching the criteria. Thesub-query is sent to each of the computing devices that stores the datablocks identified by the indices from the index database.

In a further aspect of an embodiment of a system in accordance with thedescription, an evaluator is run on each of a plurality of computingdevices that store the data blocks. The evaluator receives the sub-queryfrom the query layer and evaluates the criteria specified in thesub-query against the data blocks stored on the same computing device asthe evaluator in order to select a subset of data that matches thecriteria. The evaluator then performs the logical operation specified inthe sub-query on the subset of data to produce a sub-result. Thesub-result is forwarded from each evaluator to an output handler thattakes these responses and combines them into a final query result.

In yet another aspect of an embodiment of a system in accordance withthe description, the time series database includes a control node and aplurality of data nodes. Each data node runs on a computing device andstores specific data blocks that are sent to the time series database.The control node chooses which data node will store each data block andrecords which data blocks are stored by each data node in a locationmap.

BRIEF DESCRIPTION OF DRAWING FIGURES

The above and other aspects, features, and advantages of the presentdisclosure will become more apparent in light of the subsequent detaileddescription when taken in conjunction with the accompanying drawings,wherein like elements are numbered alike in the several figures, and inwhich:

FIG. 1 is a schematic illustration of the logical system architecture inaccordance with one embodiment of a storage and query system asdescribed herein;

FIGS. 2-4 show schematic illustrations of specific arrangements ofcomputing devices that may be used in various exemplary embodiments ofsystems corresponding to the logical systems of FIG. 1; and

FIG. 5 is a schematic illustration of a service architecture inaccordance with one embodiment of a storage and analytic service asdescribed herein.

DETAILED DESCRIPTION

As discussed above, storage of increasingly large volumes of data thatare generated during the operation of modern systems continues to stressstorage and retrieval capabilities. For example, running a single powerturbine can generate tens or hundreds of megabytes of data per day ofoperation. In addition to monitoring the values in real time for controlpurposes, it is desirable to be able to store such data so that theperformance of the equipment can be analyzed over time. Such monitoringand storage can provide the ability to diagnose failures, schedulemaintenance effectively, and predict the need for replacement of partsor entire turbines. In addition to being used locally with regard to asingle machine, such as a turbine, such information can be passed to acentral location for comparison across an entire fleet of equipment,such as turbines, railcars, trucks, jet engines, or any other assetwhich generates data. For instance, in a hospital, individual patientscould be considered assets, and the medical monitoring equipment couldbe used to generate data describing their state over time.

However, in order to perform the desired analytics, the information mustbe not only be stored, preferably without having to decimate orotherwise degrade the original data, but it also should be stored in away that preserves the critical information related to when the data wasmeasured. Because the time at which a piece of data is captured isneeded to properly assess this type of information, it is referred to as“time series” data.

Time series data can correspond to any measurements that are generallytaken at a sampling rate, and for which the time of the measurement isnoted and stored. As used herein, “sensor” will refer to any devicewhich captures operating data related to some monitored asset. Suchsensors can capture measurements related to the properties of an asset,whether those properties are physical such as vibration or speed,operational such as an operating mode or command status (such as athrottle setting), or circumstantial, such as location or ambienttemperature. An “asset” can be anything for which periodic measurementsare desired and are captured. Assets could include items from individualmachines or portions of machines (turbine stages, for instance), toentire systems such as a power plant, or even systems of systems, suchas a power grid. Another example of an asset could be a patient at ahospital who is undergoing tests, or who is connected to a monitoringsystem of some kind.

Storing the data generated by a set of assets requires that theinformation be passed to a storage system and stored as fast as, orfaster than, it is generated. Failure to do so will ultimately result ina loss of data if the data is generated continuously. Althoughhistorically it was often possible for a single machine, or a smalldedicated cluster of machines to receive and store such data in realtime, the increased volumes of such data will eventually overwhelm anysingle machine, no matter how large or capable.

Therefore, it is desirable that any such system be capable of fast readand write performance so as to capture data from multiple assets in realtime. In the described systems, one way to do this is to use adistributed storage system that makes use of a time series database thatcan be scaled across multiple computing devices. In such an embodiment,the storage architecture can be expanded by adding additional computingdevices as needed if the total volume or data rate required increases.

One embodiment of a system in accordance with the description herein isshown in FIG. 1. A storage, query and analysis system 100 is shown. Aswill be discussed below, the various components illustrated in FIG. 1include components that are described herein functionally. The functionsdescribed may be executed by dedicated hardware, or by processes runningon other more general hardware. In general, throughout the descriptionherein, “computing device” or “computer” will be used to refer to anycomputing hardware that is used to execute programs to perform thefunctions of any of the elements described.

The system 100 of FIG. 1 is fed information from a data generator 110,which may be part of the system in certain embodiments. The datagenerator produces time stamped data 115 that reflects an aspect of theassets 120 being monitored. As discussed above, the data generator canreceive information from multiple sensors 125 associated with a singleasset, as well as sensors associated with multiple assets. As eachmeasurement or other reading is received, the data generator attaches anappropriate time stamp, if one was not already attached to the data. Thedata generator operates in real time (or as close to real time aspossible) and generates a flow of data associated with the monitoredassets.

The data generator 110 passes this time stamped data 115 to an ingester130. The ingester receives the time stamped data and separates the datait receives into data blocks 135 and indices 140 that are associatedwith the data block. Each data block 135 contains time stampedinformation that shares a particular set of metadata. Metadata describesthe data being stored, for instance, the source of the data (e.g., theasset 120 that generated the data), the sensor 125 type, the owner ofthe asset that generated the data, a time range that the data fallswithin, a profile of the asset that generated the data in the data block135, and so forth. Metadata may even include information such as aparticular previous query or request that generated the particular blockof data (as will be discussed further below). Such metadata capturesinformation that will be useful when identifying what type ofinformation can be found within a particular data block.

The ingester 130 generates an index 140 for each data block 135containing information that shares appropriate metadata. The indexinformation generated by the ingester is sent to an index database 145.The data block 135 is passed to a time series database system 150 forstorage.

In some configurations, the ingester 130 may receive information frommultiple data generators 110 (only one is shown in FIG. 1 forsimplicity). It is also possible in some variations that multipleingesters (not shown) may operate within the same system. However, it isgenerally desirable that all metadata be stored as indices 140 in asingle index database 145 for a system 100. The index database may be arelational database, or any other database system that is suited tostoring the index information. Note that the index database does notgenerally contain the time series data itself (which is stored in thedata blocks 135), but only contains metadata that can be applied to anentire data block. As a result, the amount of information that is passedto the index database for a given volume of actual time series data issignificantly smaller in volume, and can be processed quickly.

The time series database 150, shown in FIG. 1, is used to store the timeseries data in the data blocks 135. The time series database, which isalso referred to as a time series data store, or TSDS, is formedlogically from a control node 155 and one or more data nodes 160. Thesenodes are logical processes that are separated computationally from oneanother; however, in various embodiments, these nodes may either resideon the same computing hardware, or be separated onto multiple computingdevices. As will be discussed below, the control node 155 may be on aseparate computing device from all of the data nodes 160, may share acomputing device with one data node, or may be located on the samecomputing device as all data nodes within the TSDS. The logicalseparation of these nodes allows for the scaling of the TSDS to use morehardware as the volume of data to be managed increases.

The control node 155 is responsible for assigning each incoming datablock 135 for storage to a specific data node 160, and to keep track ofwhich data blocks are stored on which data nodes. The control node inthis way creates and stores a map 165 of the logical storage of datablocks within the TSDS. Once the control node decides which data nodewill store a given data block, the data block is passed to that datanode and stored.

Because the control node 155 does not need to store the data blocks 135,and really does not need to process the data within the data blocks atall, the control node does not require the ability to process theentirety of the stored data. Similarly, because each data node 160 onlyneeds to store the particular data blocks 135 that are assigned to it bythe control node, no data node needs the ability to process the entiretyof the stored data. Because these processing and storage loads arebroken up in this manner, it is possible to construct a TSDS 150 thatuses multiple machines to achieve storage capability with throughputthat is many times greater than would be possible if a single devicewere used to handle the entire TSDS.

In addition, because the capability to handle greater volumes of data inthe incoming data blocks 135 is increased with the additional of morecomputing devices to the TSDS 150 (to act as additional data nodes 160),the TSDS is scalable, without a need to change the process describedherein, even if more computing hardware is added.

The portion of the system of FIG. 1 described so far addresses thestorage of the data generated from any data generators 110 that feed thesystem 100. However, it is desirable that systems that process suchvolumes of time series data (often referred to as “historians”) are alsocapable of performing data retrieval, and more importantly, analysis onsuch data. The architecture shown in FIG. 1 and described herein alsoprovides improved ability to perform analytics on the stored data inresponse to custom queries, as will be discussed.

The system 100 of FIG. 1 also includes a query layer 200 that receivesrequests to extract subsets of data from the system and perform analysison that data. The requests made to the query layer take the form ofqueries 205 that contain a set of criteria identifying the stored datathat they wish to analyze, and some analysis that is to be performed onthe retrieved results.

Criteria may include any type of selection logic based on theinformation that is associated with the stored data. Criteria used whenextracting or analyzing time series data will often include particulartime restrictions, for instance, data that was generated on a particulardate between noon and 8 PM. Criteria may also include other informationthat identifies the particular data to be analyzed, for example: whichassets 120 the data was generated from, what type of sensor 125generated the data, which asset profiles should have their datasearched, etc. In general, criteria that correspond to metadata that isstored in the index database 145 will result in the most effective useof the system described herein.

The analysis may be a mathematical or logical function that is to beperformed on the data that meets the specified criteria. These mightinclude, for instance: summation, averages, extreme values, deviations,and so forth.

The query layer 200 uses the criteria 210 that it receives to requestfrom the index database 145 the identity of the data blocks 135 that maycontain time series data that meet the criteria. When the criteria arecompared to metadata of the types that are stored within the indexdatabase, it becomes possible to identify those data blocks which cannotcontain time series data that is relevant to the query 205, and thosethat may contain data relevant to the query. The identifiers 215 ofthose data blocks which are relevant are sent back to the query layer.

The query layer 200 then prepares a sub-query 220 that can be performedon the data within each data block 135 that potentially containsrelevant data (as identified by the data block identifiers 215 returnedfrom the index database 145). The sub-query will contain the criteria210 received in the original query, as well as a logical operation thatshould be performed upon the data matching the criteria in each datablock 135. This logical operation is based upon the analysis requestedin the original query 205, but may not be identical. For instance, ifthe analysis requested was an average value in the query, the sub-query220 might have a logical operation that requested an average of the datamatching the criteria and a count of the number of elements matching thecriteria; both of these pieces of information are needed to reconcilethe sub-averages from each data block into a final average. This isdiscussed further below.

The sub-query 220 is then sent to the TSDS 150 along with the list ofdata blocks 215 that contain relevant data. The TSDS receives thissub-query at the control node 155, and then uses the information in thestorage map 165 regarding which data nodes 160 contain which data blocks135 to pass the sub-query request on to each data node that contains theappropriate data blocks.

The data nodes 160 that receive the sub-query 220 process the sub-queryusing an evaluator 225 process. The evaluator reads the indicated datablock 135 and performs the logical operation specified in the sub-queryon all data in the data block that matches the criteria 210. Each datablock will contain a different subset of data that matches the criteria,and each evaluator will process that subset and produce a sub-result 230that includes the result of applying the logical operations to thesubset of matching data in its data block. Note that for simple requestsfor data, or for analytic queries that cannot be broken down intosub-operations (because the raw data requires analysis as a body), thesub-results will consist of the raw portions of the data blocks thatcontain the requested information. In such cases, no processing oflogical operations will take place at the evaluator.

The evaluator 225 will then pass this sub-result 230 back to an outputhandler 235. In some embodiments the output handler is part of the querylayer 200. The output handler will receive the sub-results 230 producedfrom each of the evaluators 225 corresponding to data nodes 160 thatcontained data blocks 135 with relevant data, and will process this datainto a final query result 240.

For example, in the case discussed above where the original queryrequested an average (analysis) of the data associated with a particularasset over a particular time (criteria), the sub-query would include theasset and time criteria, as well as logical operations of an average anda count of matching data points. When this sub-query was executed byeach evaluator on the data nodes that contained data blocks that theindex database indicated contained possibly matching data, theevaluators would each produce an average value for the matching datawithin their data block, and a count of the matching data within theirdata block. This average and count would be passed to the outputhandler.

The output handler would then average the received sub-results,weighting each average value provided by the count of the data pointsthat the average represented. The end result is an average of all of thedata that matches the criteria across all data blocks within the system.However, in cases such as this where the output handler did not have toreceive or process any of the individual data that matched the criteria,handling only the pre-processed sub-results, it could operate quicklyand without requiring very high data volume. This benefit may not berealized in all circumstances, for instance when the analysis requirescollecting the entire body of data results prior to any processing (orwhen a query that simply requests data is made). Similarly, because theTSDS only had to run the sub-query on those data nodes that the controlnode indicated contained data blocks with potentially useful data, thedata nodes could share the search and calculation load, and there was noneed to transfer data between data nodes.

As a result, the final result 240 generated is produced with lowerlevels of system overhead and network traffic within the system, whichprovides for several benefits. Firstly, in embodiments that make use ofcriteria 210 that match metadata stored in the index database 145, it isoften possible to minimize the number of different data blocks 135 thatmust be accessed, which improves performance speed. Secondly, inembodiments that make use of multiple data nodes 160, computation isshared among the evaluators 225 on each node, also improving systemresponse speed. By not having to move the individual data blocks acrossmultiple nodes for collective processing in one central location,network traffic is minimized, avoiding network bottlenecks that couldslow down the speed of generating query results.

Finally, because bottlenecks are avoided in data traffic and computationand searching within the data blocks 135 is distributed, the system 100as a whole can process requests 205 involving larger amounts of data byadding more machines to the TSDS 150. This scalability allows for thesystem as a whole to be configured in a way that can be tailored to theuses expected. It also allows for the system to grow as greater demandsare placed on the system, due to increased numbers of assets or sensors,higher sampling rates, or accumulating data over a long operationalperiod.

Examples of various ways that storage and analysis systems in accordancewith various embodiments are shown in FIGS. 2, 3 and 4. FIG. 2 shows asystem 300 that includes a TSDS that has three data nodes and a controlnode distributed across four computing devices. The ingester, querylayer, index database and output handler are also distributed acrossthese same four computing devices.

FIG. 3 illustrates an embodiment of the described system 350 where theingester, query layer, and index database each run on separate computingdevices from each other, and also run on separate computing devices fromthe TSDS 360, which is implemented across four devices, similar to theTSDS 150 of FIG. 2.

FIG. 4 illustrates a system configuration 400 with multiple query layers410 running on separate machines, and an index database 420 on its ownmachine. Multiple ingesters 430 feed data to the index database and aTSDS 440 comprising six devices (one control node 450 and five datanodes 460, each on separate devices). The TSDS returns sub-results to asingle output handler 470 for preparation of a final result. It shouldbe appreciated that different configurations will provide differentlevels of benefit depending on the amount of data to be stored at anygiven time, the nature of the queries and analysis run on the data, andthe structure of the data itself.

Also, it should be recognized that the distribution of informationacross various logical structures that can be implemented on multiplecomputing devices can provide for a degree of fault-tolerance as well asscalability.

In addition to the systems described with respect to FIGS. 1-4, above,variations on the logical system structure can also be made within thescope of the concepts described. For instance, FIG. 1 shows that theingester 130 may be configured to receive data read from an existingexternal database 170 of time series data. Such an approach may beuseful when existing systems that are not scalable are to be replaced bya query and analysis system 100 as described herein. By reading the datafrom the external database 170 and then processing it into data blocks135 with appropriate metadata associated with the data blocks beingstored in the index database 145, conversion of unsuitably structureddata can be performed.

In addition, query results 240 that are generated by the system may bepassed out of the system in various forms. In one embodiment, a finalresult is formatted and presented directly to a user via a userinterface (not shown). In another embodiment, the analytic result isdelivered as a set of time series data itself, and can either bedelivered to external systems (not shown) for further processing, or fedback into the ingester 130 for storage within the system 100. Suchstorage of previous results with appropriate metadata can help ingenerating results for future queries and for composing compound queriesthat are dependent on previous results, without the need to execute theprior queries each time.

The flexibility provided in such a logical architecture as shown in FIG.1 also allows the system 100 to be run in a way that offers time seriesdata storage and analytics as a service, without the need for dedicateddatabase hardware to be run by the customer. A schematic of such aservice is shown in FIG. 5 and discussed below.

The service is a functional offering that may reside remotely from theusers. As shown in FIG. 5, the entire service 500 is a closed box asviewed by a user of the service, who has access only to the inputs andoutputs of the service offered. Such an arrangement may also allow theservice to be packaged as a single appliance or turnkey system 510 thatmay be run by a service provider, and changed as needed to maintain orimprove performance capabilities without a need to alter the serviceofferings as provided to the service customers.

The service 500 provides a data pipe 520, to which a customer's datagenerators (not shown) may be connected for storage of the data producedby the generators. The data, once passed to the service across the datapipe, which may be implemented as any type of network or other remoteconnection, are passed to the ingester (or multiple ingesters inalternate embodiments) of the service provider. Once received, the datapassed into the service are handled by the system in the same manner asdescribed above with regard to FIG. 1. The appliance or system 510 thatis operated by the service provider may be implemented as any one of thesystem 100, 300, 350, 400 architectures described above in FIGS. 1-4, oras any other variation that makes use of any of the architecturesdescribed above.

A query pipe 530 is also connected to the service 500, and a requestormay send query requests to the system via such a connection. Similar tothe arrangement of the ingester to the data pipe 520, the query pipe isconnected to the query layer (not shown). The query layer of the servicehandles received queries as described above, with the results beingprepared by the system or appliance of the service provider.

Once a final result is produced by the output handler, the serviceprovider has the output handler pass the result back to the requestorvia a result pipe 540. The requestor may process this data as they seefit, including resubmitting it to the data pipe 520 for storage by theservice 500 if desired.

Although not required by such a service 500, the model of use providedby offering the storage and analysis as a service can provide benefitsnot only to particular customers who do not want to run their ownsystems, but also because the data provided via service may spanmultiple devices, equipment owners, data sources and so on. Althoughthese users may consider their own data and queries to be entirelyseparate from other users of the system (and they would not generally beprovided with access to the data of others), there may be other userswho can benefit from being able to run analytics across the body of datathat spans multiple service customers.

For example, multiple local power utilities may run equipment boughtfrom a single source, and may contract with the supplier to providemaintenance through a service agreement. If the supplier wished to usethe data generated by the equipment to perform prognostics and otheranalysis to assist in maintenance, diagnostics and repair of eachutility's equipment, either a system as described above could be run foreach utility, or each utility could subscribe to such a service. Thiswould allow for analysis of each utility's equipment.

However, if all such data were stored in a way that was transparent tothe supplier—for instance if the supplier were the data service provideras well—not only could the supplier execute on each the service contractfor each utility exactly as if separate instances of the system were runfor each utility, but certain benefits could be realized for both theutilities and the supplier. The use of a single, larger system wouldprovide fault-tolerance and excess processing capability that couldimprove response times for all users. A greater degree of faulttolerance could be achieved without the need for separate instancesbeing individually made redundant.

In addition to these benefits, the supplier could compare data acrossits customers' equipment. This could provide insight into improving theoperation of the equipment, as well as better predicting maintenanceneeds across the fleet, and producing better compliance with its serviceobligations.

In various such embodiments, it may be desired to use the query andindex database to provide for authentication and authorizationfunctions. This allows each user to see their own data within thesystem, but prevents them from receiving results based on data to whichthey do not have permission. This also allows for other accessrestrictions, based on factors such as service level purchased (advancedanalytics costing more), ownership of data, regulations (for instanceHIPAA or other regulations regarding data privacy and access), etc.

The various embodiments described herein may be used to provide moreeffective storage and analysis of time series data. They may also beused to provide scalable systems that can be used to provide service tolarge client bases. Any given embodiment may provide one or more of theadvantages recited, but need not provide all objects or advantagesrecited for any other embodiment. Those skilled in the art willrecognize that the systems and techniques described herein may beembodied or carried out in a manner that achieves or optimizes oneadvantage or group of advantages as taught herein without necessarilyachieving other objects or advantages as may be taught or suggestedherein.

This written description may enable those of ordinary skill in the artto make and use embodiments having alternative elements that likewisecorrespond to the elements of the invention recited in the claims. Thescope of the invention thus includes structures, systems and methodsthat do not differ from the literal language of the claims, and furtherincludes other structures, systems and methods with insubstantialdifferences from the literal language of the claims. While only certainfeatures and embodiments have been illustrated and described herein,many modifications and changes may occur to one of ordinary skill in therelevant art. Thus, it is intended that the scope of the inventiondisclosed should not be limited by the particular disclosed embodimentsdescribed above, but should be determined only by a fair reading of theclaims that follow.

The claims are as follows:
 1. (canceled)
 2. A method of offering a dataservice for storage and querying of real-time asset operational data,comprising: accessing, from a data source, time stamped data; creating aplurality of data blocks, each data block comprising a portion of thetime stamped data with at least one common metadata attribute and aplurality of indices, each index associated with a corresponding datablock, each index comprising metadata associated with the correspondingdata block; storing the plurality of indices in an index database;storing the plurality of data blocks in a time series databasecomprising a plurality of computing devices, each of which stores aportion of the plurality of data blocks; receiving, at a query layerfrom a requestor, a query that specifies criteria defining a set of dataretrieved from the service; requesting from the index database theindices associated with data blocks stored in the time series databasecomprising time stamped data accessed to evaluate the query; preparing asub-query that produces appropriate data matching the criteria, thesub-query comprising the criteria and a logical operation performed ondata matching the criteria; sending the sub-query to an evaluatorresident on each of the computing devices that corresponds to the datablocks identified in the requesting operation; receiving at theevaluator the sub-query from the query layer; evaluating the criteriaspecified in the sub-query with respect to the data blocks stored on thesame computing devices as the evaluator in order to select a subset ofdata; performing the logical operation specified in the sub-query on thesubset of data to produce a sub-result; returning the sub-result to anoutput handler; receiving, at an output handler, the sub-resultsproduced in response to each sub-query; combining the sub-results into aquery result; and returning the query result to the requestor.
 3. Themethod of claim 2, wherein the index describes metadata associated withthe time stamped data.
 4. The method of claim 2, wherein the metadataincludes at least one of the following types of information: the assetgenerating the data in the data block; the sensor type associated withthe data in the data block; an asset profile associated with the data inthe data block; a time range covered by the data in the data block; aquery associated with the data in the data block; an owner associatedwith the data in the data block; and a security setting associated withthe data in the data block
 5. The method of claim 2, wherein the timeseries database comprises: a plurality of data nodes, each consisting ofone of the plurality of computing devices, on which specific data blocksare stored; and a control node configured to choose which data node willstore each data block and to record which data blocks are stored on eachof the plurality of data nodes.
 6. The method of claim 5, wherein thecontrol node and each of the plurality of data nodes run on a separateone of the plurality of computing devices.
 7. The method of claim 2,wherein the requesting operation comprises comparing the criteria tometadata stored within the index database to identify data blocks thatmay contain data relevant to the query.
 8. The method of claim 5,wherein the control node is configured to assign storage of each datablock to multiple data nodes.
 9. The method of claim 2, furthercomprising delivering the query result as a set of time stamped datainto the method of claim
 2. 10. The method of claim 2, furthercomprising forwarding the query results to an external system.
 11. Themethod of claim 2, wherein the index database is running on one of theplurality of computing devices.
 12. The method of claim 5, wherein theindex database is running on the same one of the plurality of computingdevices as the control node of the time series database.
 13. The methodof claim 2, wherein the query layer is running on one of the pluralityof computing devices.
 14. An appliance configured to perform the methodof claim 2, the appliance comprising at least one processor andassociated memory.
 15. The appliance of claim 14, wherein the applianceis connected to the data source, the data source being a data generatorcomprising a plurality of sensors measuring operational parameters of aplurality of assets in real time.
 16. The appliance of claim 14 whereinthe appliance is connected to a plurality of data generators.
 17. Asystem providing a service for storage and querying of real-time assetoperational data, the system comprising: a data pipe comprising aconnection through which data flows from a data source to an ingester,the ingester configured to receive data and separate the data into datablocks by performing operations comprising: receiving, via the datapipe, time stamped data from the data source; reading the received timestamped data and metadata associated with the time series data; creatinga data block based on a common metadata attribute shared by data in thedata block and an index associated with the time stamped data, the indexcomprising metadata associated with the data block; storing the index inan index database; and storing the data block in a time series databasecomprising a control node and a plurality of data nodes; a query pipe,comprising a connection through which a query flows from a requestor toa query layer, the query layer configured to execute the query byperforming operations comprising: receiving, via the query pipe, thequery that specifies criteria defining a set of data stored in the timeseries database and an analysis to be performed on the set of data;requesting from the index database the indices associated with datablocks stored in the time series database comprising time stamped dataaccessed to evaluate the query; preparing a sub-query that producesappropriate data matching the criteria, the sub-query comprising thecriteria and a logical operation performed on data matching the criteriathat allows the analysis to be completed; sending the sub-query to anevaluator resident on each data node that corresponds to data blocksidentified in the requesting operation, above, each evaluator configuredto execute the sub-query by performing operations comprising: receivingthe sub-query from the query layer; evaluating the criteria specified inthe sub-query with respect to the data blocks stored on the same datanode as the evaluator in order to select a subset of data; performingthe logical operation specified in the sub-query on the subset of datato produce a sub-result; and return the sub-result to an output handler;and a result pipe, comprising a connection through which a query resultflows from the output handler to the requestor, the output handlerconfigured to generate the query result by performing operationscomprising: receiving the sub-results produced in response to eachsub-query; combining the sub-results into the query result; andreturning, via the result pipe, the query result to the requestor. 18.The system of claim 17, wherein the common metadata attribute of thedata block comprises at least one of the following types of information:the asset generating the data in the data block; the sensor typeassociated with the data in the data block; an asset profile associatedwith the data in the data block; a time range covered by the data in thedata block; a query associated with the data in the data block; an ownerassociated with the data in the data block; and a security settingassociated with the data in the data block.
 19. The system of claim 17,wherein: the time stamped data is received from a plurality ofsubscribers to the service; the requestor is one of the plurality ofsubscribers to the service; the requestor is authenticated so that therequestor is allowed to execute the query against their own data but areprevented from seeing others' data; an operator of the service isauthorized to run queries and analysis across all stored indices anddata.
 20. The system of claim 17, wherein the analysis and the logicaloperation are different and wherein the logical operation is apre-requisite to the analysis.
 21. The system of claim 19, wherein thecontrol node is configured to assign storage of each data block tomultiple data nodes.